Project Info

Project Description

CAS29: Cycling Augmentation System

It's 2029. You've overslept your alarm It's 8.59am. You're late for work. Speeding through your morning routine you get to the door - but how are you going to travel to work today...

You could drive, but the traffic is awful. A bus or tram is a tempting proposal, but they're also full. What do you do? You turn to the pinnacle of modern transportation - the bicycle.

Did you know?
The development of the 'safety bicycle' in 1880 was amongst the most important invention in the history of the bicycle. It shifted public perception and use from being a dangerous toy for sporting young men to the herald of everyday transport today.

Much like the development of the 1880s safety bicycle, CAS29 is revolutionising cycling today. This concept connects the modern cyclist with live safety information based on your location. Why not cycle when CAS29 has your back?

Our vision

The future of public transportation in Australia will be cycling! As our cities expand, and our work cubicles shrink, there will be more pressure on our transportation network. We believe that cycling will offer the highest autonomy to the modern commuter and will serve to compliment developing public transportation options such as trains, trams and buses.

However, with a growing population, our cities will have more cars, buses and pedestrians sharing our roads. Providing a safe environment for all commuters will be a key challenge.

Product Description

We have created a concept app implementing historical road crash data, weather data and machine vision to provide a summary of crash events and alerts as your cycle along your route.

We envision that as your ride your bike, your mobile device running a client application periodically checks with the server. The server receives your location and the current time of date and filters historical pedestrian and cyclist crash data within a geographical radius and time window for that location. The server pushes this back to the client, along with weather conditions sourced from the Bureau of Meteorology and draws on smart object detection from your device to provide a contextually relevant voice prompts of potential risks as you cycle.

Our API server is updated daily via ACT SODA APIs and every 30 minutes from the BOM meteorology data feed. The API server is able to provide statistics on crash events around the provided geographic coordinates along with their severity (whether property damage, injury or fatalities were sustained). The smart object detection can perceive other road uses, such as cars, cyclists, pedestrians or even the run away pet, to ensure you have a second set of eyes watching your back.

Data Story

Who are the users?

The Simpson’s Story…
Marge and Homer have two young kids. Ferrying their kids too and from school each day is a serious undertaking. To make matters worse their kids have expressed that it's no longer 'cool' to be dropped off at school. Parenting is tough. Marge and Homer would love their kids to be able to cycle to school, but want to make sure that their commute is safe.
CAS29 provides the reassurance that their kids will be safe and aware. A calming voice alert from CAS29 will detect any obstacles or dangers and remind the kids to be careful. With the confidence of CAS29 Marge and Homer are free to enjoy their mornings

Cathy’s Story...
Cathy recently graduated from her university degree and moved to Canberra for a new job. She was excited to get started crunching spreadsheets and purchased a new bike to commute to work. Cathy being new to the city was worried about her safety whilst commuting.
After installing CAS29 Cathy could receive live notifications whenever she got near accident hot spots. With the confidence that the ride would be safe, and with weather updates helping her stay dry, Cathy could arrive at work energised and ready for eight hours of data cleansing.

Beetle Bikes Story...
Beetle Bikes, a small business renting bikes to tourists, found that those unfamiliar with the city ended up in accidents. Concerned for the safety of their customers (and hopeful to reduce damage to their bikes) Beetle Bikes turned to CAS29. Their rental bikes can now alert riders to potential collisions on the road through CAS29's machine vision. Beetle Bikes are happy to see their customers enjoying the sights of the city, and getting back in one piece.

Why is this a valuable tool?

CAS29 assists those seeking a safer cycling experience. The API collates historic road crash data within a geographic radius around the users position, and filters based on the current time of day. The CAS29 API also pulls recent BOM weather data to provide to the client. CAS29 extends the value of this historic data by leveraging machine vision to assess the local environment to provide contextual alerts (ie. that another cyclist is behind you, or someone has stepped out onto the road ahead).

Public Transport for The Future (AWS)Challenge: How might we combine data with modern technologies - such as AI/ML, IoT, Analytics or Natural Language interfaces - to better our public transport services.

CAS29 features an object recognition deep learning model (based around YOLO v3) to determine potential on-road risks for cyclists. The recognition model aims to provide two key functions:
(1) A cyclist has a limited range of vision and can't always see behind or too their sides. The potential to mount small cameras or detectors to the bike and leverage machine vision to detect objects would allow CAS29 to provide an extra set of eyes for a cyclist.
(2) Whilst notifications about historical crash data provides a useful contextual understanding of the current area, we're focused on delivering a good customer experience. Machine vision provides the application with the current road context, meaning prompts about potential risks will only be provided if useful. For example, if there aren’t any cars on the road then CAS29 won’t bug the cyclist with historical crash data.

Queensland OpenAPIChallenge: Create a project using one or more of Queensland's Open-APIs.

Currently, CAS29 uses open data from data.act.gov.au to produce a statistical summary of crash events within a radius around the cyclist. The CAS29 server regularly updates the crash data through a SODA API. This approach could be easily scaled to a national level by pulling on data APIs from other local, state and national sources.

We'd envision that Queensland's historical crash data, particularly crashes involving cyclists, could be combined with the QLDTraffic GeoJSON API to deliver real-time updates on traffic events and high-risk crash zones, or even provide live webcam traffic feeds to help a cyclist plan a safe journey.

Note: we did find some difficulty locating non-aggregated data for road crashes in NSW :(

Reducing CBD traffic congestionChallenge: How to reduce traffic congestion or parking problems in the CBD?

The easiest way to reduce traffic congestion and parking problems is to reduce the numbers of cars on the road during peak times. Cycling is a fantastic way for people to commute to school or work, and gets you healthy to boot!

Our vision for CAS29 is that by making the cycling experience safer for all commuters we could see high adoption of cycling as the primary mode of transport. CAS29 combines location and time specific road crash data along with live input from an object recognition deep learning model to help alert cyclists to potential dangers.

In the future, we could configure CAS29 to include live information about potential risks on their route such as QLDTraffic GeoJSON API , or even to offer suggestions for alternative routes. Keeping cyclists safe, and getting more people onto bikes, will see a reduction in traffic congestion for all commuters.

Canberra 2029 - Inclusive, Progressive and Connected

Challenge: How do we use data from the past to predict a better future for Canberra? How do we best support the diversity of our community? Optimise the way we travel and transport goods throughout our city? Predict the jobs of the future - and skills needed for them? Connect our citizens with their environment?

The future of Canberra is going to see a much higher presence of cyclists on our roads (and hopefully a few less cars!). Cycling gives an opportunity for Canberreans to enjoy the environment of the fable 'bush capital' during their daily commutes, and provides far more autonomy over transport options as future workplaces change.

To help create this better future for Canberra, CAS29 will help keep cyclists safe and allow them to enjoy the ride. A safer cycling experience will help foster a stronger community and encourage people to make the jump across to cycling.

CAS29 draws on historical pedestrian and cyclist crash data from data.act.gov.au for a user's current location and time of day and combines this with an object recognition deep learning model to recognise potential hazards and warn the commuter. CAS29 delivers voice prompts to warn cyclists, meaning their eyes don't come off the road. By drawing on open data we're hopeful that CAS29 could support cyclists of any background or ability to feel safe and confident on the road whilst they commute around Canberra.

Team DataSets

Crash data from Queensland roads

Description of Use: We have explored the potential to incorporate this data source into CAS29, so that QLD cyclists can also benefit from information on historically hazardous zones. This data can be filtered for crashes involving cyclists and/or pedestrians and identifies the severity of the crash, as well as the time-of-day and latitude/longitude co-ordinates. With this data, CAS29 will be able to use a QLD cyclist's time and location to alert the cyclist of crash hazards.

QLDTraffic GeoJSON API

Description of Use: We have explored the potential to incorporate this data source into CAS29, so that QLD cyclists can benefit from real-time updates regarding hazards and other relevant traffic information. The updates from the QLDTraffic GeoJSON API include geo-location co-ordinates. With this data, CAS29 will be able to use a QLD cyclist's location to pass on any alerts via this API.

Cyclist Crashes

Description of Use: CAS29 uses the dataset to produce the historical summaries of cyclist crashes within a set radius and time interval of your location and time of the ride. The dataset is further filtered by the type and severity of the crash and the summary is provided live to the cyclist.

Pedestrian Crashes

Description of Use: CAS29 uses the Pedestrian Crashes dataset to produce the historical summaries of pedestrian crashes within a set radius and time interval of your location and time of the ride. The dataset is further filtered by the type and severity of the pedestrian crash and the summary is provided live to the cyclist or pedestrian.